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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JIKO (Jurnal Informatika dan Komputer) ILKOM Jurnal Ilmiah Journal of Information Technology and Computer Engineering Patria Artha Technological Journal Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali JURNAL IT: Media Infromasi STMIK Handayani Makassar Jambura Journal of Electrical and Electronics Engineering Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Information System Journal (INFOS) Bulletin of Information Technology (BIT) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Penelitian Ilmu Pendidikan Indonesia Jikom: Jurnal Informatika dan Komputer Nobel Community Services Journal semanTIK Edumaspul: Jurnal Pendidikan Jurnal JEETech
Hazriani, Hazriani
Universitas Handayani Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Peramalan Tren Penjualan Menu Restoran Menggunakan Metode Single Moving Average Apriliani, Aulia; Zainuddin, Hazriani; Agussalim, Agussalim; Hasanuddin, Zulfajri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722732

Abstract

Penelitian ini bertujuan untuk meramalkan tren penjualan menu pada restoran guna membantu pihak pengelola restoran dalam menentukan dan memberikan rekomendasi pengelolaan stok menu. Peramalan dilakukan dengan mengimplementasikan metode single moving average pada data transaksi penjualan selama periode 15 bulan, yakni bulan Januari-Desember 2018 dan Januari-Maret 2019 untuk menghasilkan ramalan bulanan dan harian. Total sampel data latih yang diolah sebanyak 10.515 record yang merupakan data transaksi penjualan pada bulan Januari-Desember tahun 2018, serta 2.246 record data bulan Januari-Maret 2019 sebagai data uji (untuk menguji akurasi ramalan). Hasil pengujian hasil ramalan bulanan untuk Top-10 menu menghasilkan perhitungan MAPE (Mean Absolut Percentage Error) sebesar 4% yang berarti tingkat akurasi sangat baik, yakni sebesar 96%. Sedangkan pengujian hasil ramalan harian menghasilkan MAPE yang cukup tinggi yaitu sebesar 39.2%, mengindikasikan nilai akurasi yang cukup rendah, yakni 60.8%. Meskipun akurasi untuk ramalan harian, masih rendah namun hasil penelitian ini dapat memberikan gambaran kepada pengelola hotel tentang rentang minimum-maksimal stok yang perlu disiapkan untuk menu tertentu pada hari-hari tertentu. Untuk memperoleh akurasi prediksi harian yang lebih akurat, penelitian ini akan dilanjutkan dengan mencoba metode lain serta menambah jumlah data latih. AbstractThis research aims to forecast sales trend of a restaurant menus to help the restaurant management in determaining and providing recommendations for managing stocks. Forecasting was performed by applying the single moving average towards fifteen months recorded data transaction, namely January to December 2018, and Januari to March 2019 to establish monthly and daily forecast. Total data training was 10.515 recods data transaction obtained from Januari to December 2018, while data testing was 2.246 record data transaction within Januari to March 2019. Result for montly forecast shows, that the average accuracy reached 96% (MAPE 4%) indicating the forecast is almost perfect. While, for daily forecast the average accuracy is only 60.8% (MAPE 39,2%) indicating that the forecast is less accurate. Although, accuracy of the daily forecast is considered less accurate, the result still can be used by the restaurant management to figure-out minimum and maximum amount of stock to be prepared for certain menus in certain days. 
Pengaruh Kecerdasan Intra Personal dan Partisipasi Aktif Siswa SMP N 1 Rantau Selatan Terhadap Prestasi Belajar Matematika Hazriani, Hazriani; harahap, Amin; harahap , Nurlina Ariani
Jurnal Penelitian Ilmu Pendidikan Indonesia Vol. 3 No. 2 (2024): Volume 3 No 2
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpion.v3i2.294

Abstract

Penelitian ini bertujuan untuk menyelidiki pengaruh kecerdasan intra personal dan keterlibatan aktif siswa dalam mencapai prestasi belajar matematika di SMP N 1 Rantau Selatan. Metode kuantitatif dengan pendekatan korelasional digunakan, dan informasi diperoleh melalui penggunaan kuesioner dan melalui pengamatan langsung terhadap siswa. Hasil analisis menunjukkan adanya korelasi positif antara kecerdasan intra personal siswa, partisipasi aktif, dan prestasi belajar matematika. Regresi linier berganda mengungkapkan bahwa kecerdasan intra personal dan partisipasi aktif siswa bersama-sama dapat memprediksi prestasi belajar matematika. Temuan ini memiliki implikasi praktis untuk pengembangan strategi pembelajaran yang mempertimbangkan aspek kecerdasan intra personal dan meningkatkan partisipasi aktif siswa. Oleh karena itu, disarankan untuk mengintegrasikan program pembinaan kecerdasan intra personal dan merancang metode pembelajaran yang mendorong partisipasi siswa secara aktif. Langkah-langkah ini diharapkan dapat meningkatkan kualitas pembelajaran matematika di SMP N 1 Rantau Selatan.
Analysis of Student Behavior Based on the History of Learning Activities in the Learning Management System Using the Pearson Correlation Method Imam Akbar; Hazriani Hazriani; Abdul Latief Arda; Ita Sarmita Samad
Edumaspul: Jurnal Pendidikan Vol 8 No 1 (2024): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v8i1.7671

Abstract

The purpose of this study is to identify student learning behavior in online learning and to determine the relationship between student learning behavior and learning achievement based on learning history data (Learning logs) on the Learning Management System (LMS), including the performance of assignments and quizzes, utilization interaction features (forums and chat), as well as active access to learning resources (files and URLs). Pearson Correlation method is used to analyze the level of relationship between learning behavior and students’ achievement. The research object is 105 students at Muhammadiyah University of Enrekang who programmed introductory information technology (PTI) courses from 5 different classes but taught by the same lecturer. The total number of processed activity histories (after data preprocessing) is 6500 records, while the total number of logs before data preprocessing is 19386 records. Correlation analysis linking student behavior to student learning achievement is quite strong and unidirectional, as evidenced by the correlation value between learning behavior and student final grades which show an average number of 0.80 and all are positive, with confidence interval values reaching95%. This shows that the higher the learning activities that students participate in in online learning (the more active), the stronger the effect on student learning achievement. It also shows that student activity in completing assignments is the variable that most influences learning achievement with a correlation value of 0.88 (very strong).
Information Extraction from Makassar Culinary Images Using Vision Transformers and Cahya GPT-2 (Visual Question Answering Case Study Tirta Chiantalia Sharief; Hazriani; Syamsul; Anas; Yuyun
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.357

Abstract

This study examines the development of a Visual Question Answering (VQA) system to extract information from images of Makassar culinary specialties by combining the Vision Transformer (ViT) and Cahya_GPT-2 models. The main objective is to integrate visual and natural language understanding so that computers can recognize visual objects (food images) and generate relevant text descriptions. The research method uses an experimental approach with a fine-tuning process of the pre-trained ViT model as a visual encoder and Cahya_GPT-2 as a text decoder. The dataset used includes images of Makassar culinary specialties such as Coto, Konro, Pisang Epe, Barongko, and Jalangkote with question and answer (QnA) annotations. Evaluation is carried out using the ROUGE metric to assess the semantic match between the model's answers and the actual answers. The results show that the developed multimodal model is able to accurately understand the image context with an average ROUGE-L score of 0.63, indicating a good level of closeness between the model's answers and the annotations. In conclusion, the combination of ViT and Cahya_GPT-2 can be an effective approach for natural language-based visual information extraction systems, especially in the Indonesian local culinary domain
Analisis Artikel Penelitian Berbasis Summarization Menggunakan Model BERT Muhammad Rijal; Hazriani; Yuyun
Jurnal Informatika dan Komputer Vol 16 No 1 (2026): April
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/jikom.v16i1.311

Abstract

The increasing number of scientific publications each year poses challenges in managing and processing information. To address this, this study analyzes the application of the Bidirectional Encoder Representations from Transformers (BERT) model for extractive summarization of scientific articles. BERT, which can understand word context bidirectionally, was used to summarize scientific articles with a dataset of articles related to Natural Language Processing (NLP). Evaluation was performed using ROUGE metrics, which measure the similarity between the model-generated summary and the reference summary. The evaluation results showed that the BERT model produced relevant and accurate summaries, with a precision of 1.00, recall of 0.49, and F1-score of 0.66 on the second article, and precision of 1.00, recall of 0.79, and F1-score of 0.88 on the first article. While the results are promising, challenges remain in the model's ability to capture writing style variations and summarize complex texts. This study demonstrates that BERT is effective for generating coherent automatic summaries, with potential for further development to enhance abstractive summarization quality.
The implementation of forward chaining and certainty factor methods to examine the covid-19 vaccination eligibility Herlina, Herlina; Yosephi, Valensa; Hazriani, Hazriani
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.973.307-313

Abstract

Screening for the COVID-19 vaccination is an important and mandatory step to ensure the eligibility status of each candidate for vaccination. The main obstacle experienced was the ineffective screening process (a long process) that resulted in the accumulation of patients. Sometimes it takes one to two hours from registration to receiving the vaccination paper. This situation certainly causes discomfort for both participants and vaccinators. Therefore, the presence of an expert system is needed to overcome these obstacles. The expert system plays a role in simplifying service flow and shortening service time. This article discusses the application of the Forward Chaining method to formulate screening parameters so that they can be used to accurately determine vaccination eligibility. Based on the results of interviews with experts (expert doctors who handle vaccination), 12 rules were obtained from the formulation of 20 historical parameters, which will then serve as a knowledge base for the Covid-19 vaccination screening expert system. The test is carried out by comparing the results of expert screening diagnoses with an expert system using 15 samples of test data. The test results using the Certainty Factor method show that the confidence level is 84% to 99%.
Identifikasi Pola Prilaku Belajar Mahasiswa Pada Platform Learning Management System Dengan Algoritma K-Means Ridho Muhammad Alam; Hazriani Hazriani; Abdul Latied Arda; Muhammad Ikhwan Mardin
Jurnal JEETech Vol. 6 No. 1 (2025): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i1.6102

Abstract

This study aims to explain students' behavior in accessing the kalam.umi.ac.id learning platform and its impact on student academic performance. It analyzes data collected from students' access to the platform during 12 sessions in one semester for the Islamic communication subject taught by Professor Surani. The data includes students' access times, access frequency, and academic performance such as assignment grades, midterm exams, and final exams. Through data processing methods, correlation analysis, and cluster optimization, the study found a positive relationship between access times, midterm exams, final exams, and assignments with students' final grades. The higher these variables, the higher the students' final grades. However, this relationship is not always consistent across different variables. In this study, the Elbow method was used to determine the optimal number of clusters by identifying the point where the variance reduction becomes less significant. Additionally, the Sum of Square Error (SSE) was analyzed to understand the sharp change followed by a gradual decrease in the value of K until stability is reached. Clustering results using the K-Means algorithm showed the presence of three student clusters based on their learning behavior. Cluster 0 is the largest, consisting of 176 students. Cluster 1 has 57 students, and cluster 2 is the smallest with 17 students. These clusters provide insights into varying student learning patterns, including differences in final grades and access frequency. These findings can be used as a basis for developing more effective and personalized learning strategies for students.
Sistem Pemantau Kualitas Udara Berbasis Internet Of Things: Studi Kasus : Tempat Pembuangan Akhir Tamangapa Kota Makassar Elsa Sera; Zahir Zainuddin; Hazriani
SemanTIK : Teknik Informasi Vol. 10 No. 2 (2024): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v10i2.85

Abstract

Penelitian ini bertujuan untuk mengevaluasi akurasi sistem pemantauan udara berbasis Internet of Things (IoT) di Tempat Pembuangan Akhir (TPA) Tamangapa, Kota Makassar. Topik ini penting karena pemantauan kualitas udara yang efektif dapat membantu mengatasi masalah pencemaran di area pembuangan sampah. Metode penelitian menggunakan data primer yang diperoleh langsung dari sensor kualitas udara di TPA Tamangapa, serta data sekunder dari literatur terkait. Penelitian dilaksanakan pada Sabtu, 9 Maret 2024, selama ±12 jam, dengan menggunakan jaringan sensor IoT yang terintegrasi dengan sensor seperti PMS5003, MQ-135, Anemometer, dan FC-37 yang terhubung ke NodeMCU ESP8266. Hasil penelitian menunjukkan bahwa sistem pemantauan berhasil memenuhi standar Indeks Standar Pencemar Udara (ISPU) dengan akurasi tinggi dan memungkinkan pengukuran parameter kualitas udara secara real-time. Sistem yang menggunakan berbagai sensor dan power bank untuk pemantauan berkelanjutan terbukti efektif. Data pemantauan yang akurat disajikan melalui antarmuka website dengan fitur notifikasi dan unduhan laporan, dengan total 12.266 entri data dari 3 titik pemantauan. Analisis data mengungkapkan rata-rata delay pengiriman data dan kinerja sensor. Kesimpulan penelitian ini menunjukkan bahwa evaluasi akurasi dan performa sensor memberikan wawasan penting untuk pengembangan solusi mitigasi pencemaran udara di masa depan dan menyediakan data krusial untuk pengambilan keputusan dalam peningkatan kualitas udara.
Optimasi LayoutLMv3 untuk DocVQA pada Tabel Statistik Melalui Fine-Tuning dan Semantic Mapping: Optimization of LayoutLMv3 for DocVQA on Statistical Tables via Fine-Tuning and Semantic Mapping Ansari A., D. Achmad; Hazriani, Hazriani; Yuyun, Yuyun
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2632

Abstract

Penelitian ini mengoptimalkan model LayoutLMv3 untuk tugas Document Visual Question Answering (DocVQA) pada tabel statistik melalui kombinasi Continual Fine-tuning (CFT) yang mengintegrasikan normalisasi koordinat 2D dan patch embedding visual, serta lapisan logika Semantic Similarity Mapping (SSM) untuk menangani variasi pertanyaan. Dataset mandiri dibangun dari 398 gambar tabel publikasi BPS "Kabupaten Pinrang Dalam Angka 2025" dengan 19.900 pasangan QA. Hasil evaluasi menunjukkan Training Loss turun dari 3,72 menjadi 0,63, ROUGE-1 mencapai 74,06%, dan akurasi human validation  meningkat dari 0% (zero-shot) menjadi 41% (CFT) dan 51% (CFT+SSM). Akurasi tertinggi 100% dicapai pada tabel dengan header tiga level. Pendekatan multimodal ini efektif meningkatkan pemahaman struktur tabel statistik, namun generalisasi pada tabel kompleks masih terbatas oleh dataset.
SISTEM REKOMENDASI PEMBINAAN DAN PENGAWASAN PELANGGAR PERATURAN DAERAH DI BAWAH UMUR MENGGUNAKAN TEKNOLOGI FACE RECOGNITION M, Muhtadin; Hazriani, Hazriani; Nasrullah, Nasrullah
JURNAL ILMU KOMPUTER Vol 10 No 1 (2024): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v10i1.303

Abstract

Penyalahgunaan peraturan daerah oleh individu di bawah umur menjadi permasalahan serius yang perlu diatasi dengan tindakan tegas dan pembinaan yang tepat. Oleh karena itu, penggunaan teknologi face recognition dianggap sebagai salah satu solusi yang potensial untuk mengidentifikasi dan memantau pelanggaran yang dilakukan oleh individu di bawah umur. Penelitian melibatkan proses pengumpulan data berupa gambar wajah dari individu di bawah umur yang terlibat dalam pelanggaran peraturan daerah. Data tersebut kemudian diolah dan diimplementasikan ke dalam sistem rekomendasi menggunakan algoritma rule-based system dan library opencv dan face recognition pada phyton dengan algoritma Convolutional Neural Network (CNN) sebagai algoritma pengenalan wajah serta algoritma Support Vector Machine (SVM) untuk membedakan anak dibawah umur dan bukan. Hasil penelitian menunjukkan bahwa sistem pengenalan yang dikembangkan mampu mengidentifikasi wajah dan membedakan anak dibawah dan bukan dengan akurasi 70%-80% dan memberikan rekomendasi pembinaan dan pengawasan berdasarkan tingkat pelanggaran dengan akurasi 80%. Dalam keseluruhan penelitian, teknologi face recognition berperan sebagai solusi terdepan dalam mendukung tugas aparat Satpol PP dalam mengerjakan tugasnya secara lebih efisien dan akurat. Dengan implementasi teknologi ini, diharapkan akan terjadi peningkatan kesadaran masyarakat terhadap aturan yang berlaku dan dapat membantu mewujudkan ketertiban dan keamanan.
Co-Authors A Muh Fajar Maulana Natsir A. Ade Rosali Saputra Abdul Latied Arda Abdul Latief Arda Abdul Rokhman Abdul Rokhman Adnan Nur Agussalim, Agussalim Ahmad Firman Ahmad, Andani Anas Andani Achamad Andani Achmad Andani Achmad Andi riski ramadani Ansari A., D. Achmad APRILIANI, AULIA Aris Susanto Ayu Hasnining Baital, Muhammad Syarif Basirung Umaternate BASRI BASRI Dedi Suarna Dhimas Tribuana Elsa Sera Elsa Sera Etika Purnamasari Fajar Husain Asy'ari Hafsah HS Hafsah. HS Hairudin, Muhammad Azhar harahap , Nurlina Ariani Harahap, Amin Hasanuddin, Zulfajri Herlina Herlina herlinah herlinah Herlinah Herlinah Imam Akbar Imam Akbar, Imam Imam T. Umagapi Ingrid Nurtanio Ita Sarmita Samad Ita Sarmita Samad, Ita Sarmita Jasman Jasman La Ija Latied Arda, Abdul Leonard Maramis Lut Faizal M, Muhtadin Mashur Razak Muh Arfah Wahlil Pratama Muhammad Alam, Ridho Muhammad Arfah Wahlil Muhammad Azhar Hairudin MUHAMMAD FUAD Muhammad Ikhwan Mardin Muhammad Rijal Munthakhabah. R, Cita St. Musliadi K H Nasrullah Nasrullah Nur, Adnan Nuradha, Nuradha Nurul Azmi Omar Wahid Ridho Muhammad Alam riswanto riswanto Salmawati Samasil Samsart Deandi Palumery SYAMSU ALAM Syamsul Tirta Chiantalia Sharief Wabula, Yuyun Wibowo, Deni Yosephi, Valensa Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun, Yuyun Zahir Zainuddin Zahir Zainuddin Zahir Zainuddin